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Title:

Linear Programming Model for Estimating High-Resolution Freeway Traffic States from Vehicle Identification and Location Data

Accession Number:

01519176

Record Type:

Component

Availability:

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Washington, DC 20001 United States

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Order URL: http://worldcat.org/isbn/9780309295154

Abstract:

The estimation of traffic state on freeway segments is widely studied as a complex nonlinear and stochastic estimation problem. A unified representation with a parsimonious explanation for traffic observations under free-flow, congested, and dynamic transient conditions is developed by capturing the essential characteristics of forward and backward wave propagation through cumulative flow count variables. New formulations are presented to use Bluetooth vehicle identification records and GPS vehicle location data on a freeway corridor with a merge and diverge. With the addition of nonnegativity and maximum discharge rate constraints, a computationally efficient linear programming model is constructed to estimate traffic states (i.e., density and traffic flow) from cumulative flow counts at each second. The proposed model is implemented and tested systematically on the basis of a real-world next generation simulation (NGSIM) data set.

Monograph Accession #:

01541211

Report/Paper Numbers:

14-5449

Language:

English

Authors:

Lei, Hao
Zhou, Xuesong

Pagination:

pp 151–160

Publication Date:

2014

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2421
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309295154

Media Type:

Print

Features:

Figures (8) ; References (26) ; Tables (1)

Subject Areas:

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Files:

TRIS, TRB, ATRI

Created Date:

Jan 27 2014 3:55PM

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